class_docstr: The Graph Neural Network Force Field (GNNFF) from the
  `"Accurate and scalable graph neural network force field and molecular
  dynamics with direct force architecture"
  <https://www.nature.com/articles/s41524-021-00543-3>`_ paper.
  :class:`GNNFF` directly predicts atomic forces from automatically
  extracted features of the local atomic environment that are
  translationally-invariant, but rotationally-covariant to the coordinate of
  the atoms.
  
  Args:
      hidden_node_channels (int): Hidden node embedding size.
      hidden_edge_channels (int): Hidden edge embedding size.
      num_layers (int): Number of message passing blocks.
      cutoff (float, optional): Cutoff distance for interatomic
          interactions. (default: :obj:`5.0`)
      max_num_neighbors (int, optional): The maximum number of neighbors to
          collect for each node within the :attr:`cutoff` distance.
          (default: :obj:`32`)
code: def reset_parameters(self):
      reset(self.node_emb)
      self.edge_emb.reset_parameters()
      for node_block in self.node_blocks:
          node_block.reset_parameters()
      for edge_block in self.edge_blocks:
          edge_block.reset_parameters()
      reset(self.force_predictor)
  
imports: import torch
  from torch import Tensor
  from torch.nn import BatchNorm1d, Embedding, Linear, ModuleList, Sequential
  from torch_geometric.nn import radius_graph
  from torch_geometric.nn.inits import reset
  from torch_geometric.nn.models.dimenet import triplets
  from torch_geometric.nn.models.schnet import ShiftedSoftplus
  from torch_geometric.typing import OptTensor
  from torch_geometric.utils import scatter
label_desc: Use image node_id 2 for calling the GNNFF obj's underlying member method code with example usage: obj.reset_parameters() without return types